The field of neuroscience is experiencing rapid growth in the complexity and quantity of the recorded neural activity allowing us unprecedented access to its dynamics in different brain areas. One of the major goals of neuroscience is to find interpretable descriptions of what the brain represents and computes by trying to explain complex phenomena in simple terms. Considering this task from the perspective of dimensionality reduction provides an entry point into principled mathematical techniques allowing us to discover these representations directly from experimental data, a key step to developing rich yet comprehensible models for brain function. In this work, we employ two real-world binary datasets describing the spontaneous neuronal activity of two laboratory mice over time, and we aim to their efficient low-dimensional representation. We develop an innovative, robust to noise, dictionary learning algorithm for the identification of patterns with synchronous activity and we also extend it to identify patterns within larger time windows. The results on the classification accuracy for the discrimination between the clean and the adversarial-noisy activation patterns obtained by an SVM classifier highlight the efficacy of the proposed scheme, and the visualization of the dictionary's distribution demonstrates the multifarious information that we obtain from it.
翻译:神经科学领域在记录神经活动的复杂程度和数量方面正经历着快速增长的神经科学领域的记录神经活动,使我们能够空前地进入不同大脑区域的动态。神经科学的主要目标之一是找到对大脑所代表的和通过简单解释复杂现象进行计算的解释性描述。从减少维度的角度考虑这项任务,为原则数学技术提供了一个切入点,使我们能够直接从实验数据中发现这些表现,这是开发丰富而可理解的大脑功能模型的关键一步。在这项工作中,我们使用了两个真实世界的二元数据集,描述两个实验室老鼠在一段时间内自发神经活动的情况,我们的目标是以高效的低维度表示方式。我们开发了一种创新的、强大的噪音和字典学习算法,用以识别同步活动的模式,我们还将其扩展至在更大的时间窗口内确定模式。SVM分类器获取的对清洁和对抗性活动模式之间区别的分类准确性结果突出了拟议方案的功效,以及字典分布的可视化展示了我们从中获取的多种信息。